CN106546567B - Plant drouhgt stress diagnostic method and device based on imaging-PAM technology - Google Patents
Plant drouhgt stress diagnostic method and device based on imaging-PAM technology Download PDFInfo
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Abstract
The invention discloses a kind of plant drouhgt stress diagnostic methods and device based on imaging-PAM technology, wherein diagnostic method is the following steps are included: the sample set plant progress dark adaptation of known drought stress diagnostic result is acquired its canopy chlorophyll fluorescence image data and extract relevant characteristic parameter by (1)；(2) according to the characteristic parameter of the chlorophyll fluorescence image data of acquisition and chlorophyll fluorescence image, plant drought stress decision model is established using Combining Multiple Classifiers；(3) the chlorophyll fluorescence image data and characteristic parameter of plant to be measured are acquired according to the method for step (1), are substituted into plant drought stress decision model and are carried out drought stress diagnosis.Plant drouhgt stress diagnostic method of the invention can not only provide chlorophyll fluorescence parameters, the visualization that fluorescence parameter can also be made to be distributed in blade space, it shows photosynthetic heterogeneity between blade surface and blade, drought stress can be diagnosed at a high speed, in high precision, in real time.
Description
Technical field
The present invention relates to plant drouhgt stress diagnostic techniques more particularly to a kind of plants based on imaging-PAM technology
Object drought stress diagnostic method and device.
Background technique
Under nature or agricultural production conditions, plant can suffer from environment-stress.Wherein arid caused by water deficit is coerced
Compel the severeest.The complexity that plant responds drought stress is relatively high, can induce a variety of physiological reactions, and with plant
Object whole life cycle has a continually changing dynamic process.
Chlorophyll fluorescence techniques have become tracking plant biology and abiotic stress under photosynthesis change it is powerful
Tool, as the sub- efficiency of maximum amount (Fv/Fm) fluorescence parameter can reflect two photosystems (mainly photosynthetical system II)
Light energy absorption, transmitting, dissipation efficiency, compared with traditional photosynthetic parameters index, chlorophyll fluorescence parameters reflection is photosynthetic machine
" inside " feature of structure.
The Chinese patent literature of Publication No. CN102598986A disclose it is a kind of using cerium improve drought environment under high sheep
The method of thatch chlorophyll fluorescence power, this method comprises: measurement Tall Fescue Leaves normal water supply, medium drought, Severe drought
Chlorophyll fluorescence kinetics parameters under stress are measured using the photosynthetic instrument of Li-6400 produced in USA, and the previous day of measurement is plant
Object is placed into darkroom, and then dark adaptation 12h or more measures initial fluorescence (Fo), maximum fluorescence (Fm) in darkroom, living through illumination
Fo ', Fm ', Fv/Fm, Fm ', Fo ', Fv '/Fm ', ETR, Φ PS II, qP and qN index are measured with Li-6400 after changing.
It is inherently anti-dry that the Chinese patent literature of Publication No. CN104007093A discloses a kind of fast quantification calculating plant
The method of non-irrigated ability, comprising the following steps: will be impregnated into the water after plant leaf blade to be measured cleaning, take out blade simultaneously after 30 minutes
The water on surface is blotted；The initial fluorescence of blade when measuring 0 level fluorescence with IMAGING-PAM modulation system chlorophyll fluorescence instrument
(Fo) and PS II maximum Photochemical quantum yield (Fv/Fm) it, is repeated 3 times；Then, above-mentioned leaves water loss is allowed, every 1 hour weight
Multiple aforesaid operations.Result was measured as reference using full water 0 hour, is calculated the opposite Fo and opposite Fv/Fm at each measurement moment, is divided
Preceding 5 hours accumulate opposite Fo (TRSF) and accumulate opposite Fv/Fm (TRPF) after full water Xiang Jia not obtained.Compare TRSF and TRPF
Numerical values recited, the intrinsic drought-resistant ability of quantitative different plants.
Above-mentioned technical proposal is the chlorophyll fluorescence parameters of single-point or multimetering blade, but plant is in drought stress
Afterwards, the chlorophyll fluorescence parameters of blade are that there are special heterogeneities, and single-point or multimetering analysis are insufficient.
Summary of the invention
It is green using leaf the present invention provides a kind of plant drouhgt stress diagnostic method based on imaging-PAM technology
Plain Imaging-PAM, and image procossing is combined, the response of quantitative analysis drought stress, while corresponding device being provided, meet not
Drought stress response analysis with plant detects.
A kind of plant drouhgt stress diagnostic method based on imaging-PAM technology, comprising the following steps:
(1) the sample set plant of known drought stress diagnostic result is subjected to dark adaptation, acquires its canopy chlorophyll fluorescence
Image data simultaneously extracts relevant characteristic parameter；
(2) according to the characteristic parameter of the chlorophyll fluorescence image data of acquisition and chlorophyll fluorescence image, using more classification
Device fusion method establishes plant drought stress decision model；
(3) the chlorophyll fluorescence image data and characteristic parameter of plant to be measured are acquired according to the method for step (1), are substituted into and are planted
Strain drought stress decision model carries out drought stress diagnosis.
In drought stress, plant can close stomata to improve water use efficiency, with the aggravation of degree of drought, light
Cooperation is caused the fluorescence intensity of leaf tissue cell to change, is also led with also gradually receiving inhibition while destroying metabolism
The structural form of plant is caused also to change.Single chlorophyll fluorescence techniques are only capable of choosing several blades and survey, and obtain
Information content is fewer；The present invention is based on imaging-PAM technology, each pixel of available entire area-of-interest
Parameter value, it means that analysis, the variation inside viewing area can be compared inside blade or canopy region.Therefore
Imaging-PAM technology can provide a large amount of information, after Data Management Analysis, can extract multiplicity, significant ginseng
Number realizes quick, high-precision drought stress diagnosis.Characteristic parameter of the present invention by extraction chlorophyll fluorescence image, input
Discrimination model is established after multiple Classifiers Combination, realizes the drought stress diagnosis of plant.
Different light sources, different light application times, available different chlorophyll fluorescence image are suitable for stress point to obtain
The parameter of analysis, preferably, acquisition plant chlorophyll fluoroscopic image data, comprising the following steps:
(1-1) opens the minimum fluorescence Fo after measurement light pulse acquisition dark adaptation；
(1-2) opens the maximum fluorescence Fm after the dark adaptation of saturated light pulse collection；
(1-3) opens actinic light, then opens the maximum fluorescence Fm ' after saturated light pulse collection light adaptation；
(1-4) opens the minimum fluorescence Fo ' after far red light pulse collection light adaptation；
(1-5) finally acquires the steady-state fluorescence Fs ' when light adaptation.
The wavelength of the described measurement light is 620nm, and the wavelength of the actinic light and saturated light is 450~465nm,
The wavelength of far red light is 740nm.
The chlorophyll fluorescence image data includes:
The sub- efficiency of maximum amount (Fv/Fm=(Fm-Fo)/Fm) absorbs luminous energy for going back protoplasm for characterizing Photosystem I I
The maximal efficiency of body quinone QA；
Photo-quantum efficiency (Φ PS II=(Fm '-Fs ')/Fm ') absorbs luminous energy for going back protoplasm for characterizing Photosystem I I
The efficiency of body quinone QA；
Non- photochemical fluorescent quenching coefficient (NPQ=(Fm-Fm ')/Fm '), for characterizing Photosystem I I heat leakage situation.
The characteristic parameter of the chlorophyll fluorescence image include: the area in canopy region, mean value, variance, textural characteristics,
Bimodal separation value, space efficiency value.
The Fv/Fm of normal plant, Φ PS II, NPQ image histogram distribution generally in Unimodal Distribution, when plant by
Then occurs multi-modal when drought stress, bimodal separation value (S) is used to characterize the peak value and ebb of the histogram distribution of image
Difference between value, space efficiency value (Wmax) are used to assess the space efficiency that plant converts light energy into chemical energy.
Bimodal separation value (S) and space efficiency value (Wmax) obtain in the following manner:
S=(μmax-μmin)/2(σmax+σmin)
Wherein, S is bimodal separation value, μmax、σmaxThe respectively average and standard deviation value in peak value region, μmin、σmin
The respectively average and standard deviation value in low peak region；
Smax=(0.87- μmax)/2σmaxFor the bimodal separation value of efficiency highest part in chlorophyll fluorescence image, Smin=
(0.87-μmin)/2σminFor the bimodal separation value of efficiency lowermost portion in chlorophyll fluorescence image, ρmax、ρminRespectively peak value
The specific gravity for accounting for overall area in region and low peak region, then space efficiency value WmaxValue are as follows:
Wmax=(Smax×ρmax-Smin×ρmin)/Smax×ρmax
In order to improve the accuracy of diagnosis, preferably, sample set plant includes healthy plant and drought stressed plants, it is good for
The quantity of health plant and drought stressed plants ratio is 1: 1.
The plant of sample set is at least 500 plants.
In order to improve the accuracy of plant drought stress decision model, preferably, being built using Combining Multiple Classifiers
Vertical plant drought stress decision model, comprising: be based on MATLAB software, characteristic parameter is distinguished into input vector machine (Support
Vector machine, SVM) classifier, naive Bayesian (Naive bayes, NB) classifier and radial base (Radial
Basis function, RBF) neural network classifier establishes base classifier, the Dynamic Weights of each classifier are adaptively obtained,
It merges to obtain plant drought stress decision model by linear weighted function.
The invention also discloses a kind of plant drouhgt stress diagnostic devices based on imaging-PAM technology, comprising:
Lighting box；
Light source is mounted on the top in lighting box, for emitting detection light to plant；
Imaging-PAM module is mounted on the top in lighting box, for acquiring the chlorophyll fluorescence image of plant；
Automatically controlled sample stage is mounted on the lower section of imaging-PAM module, is used for support plant, automatically controlled sample stage and leaf
The distance between green element fluorescence imaging module is adjustable；
Computer, the image information acquired by analyzing processing from imaging-PAM module, coerces plant arid
Compel to be diagnosed；
Data acquisition module is connected with imaging-PAM module and computer respectively, for by chlorophyll fluorescence at
As the image information real-time Transmission of module acquisition is to computer；
Control module is connected with light source, imaging-PAM module and automatically controlled sample stage respectively and controls its work.
In order to realize dark adaptation and cut down illumination reflection, preferably, the inner wall of lighting box is black and frosted.
The imaging-PAM module includes CCD camera, camera lens and filter wheel, for acquiring the chlorophyll of plant
Fluorescent image.
Preferably, the light source is mounted on light source board, the geometric center hollow out of light source board, the chlorophyll is glimmering
Light image-forming module is mounted on the hollowed out area of light source board.
Light source board is " returning " font, and center hollow out, CCD camera is mounted on light source board center hollow part.
Preferably, the geometric center on light source board around light source board is separately installed with:
Blood orange light LED light, generation wavelength are the measurement light of 620nm；
White LED lamp, generation wavelength are the actinic light and saturated light of 450~465nm；
Far infrared LED light, generation wavelength are the far red light of 740nm.
Blood orange light LED light, white LED lamp and far infrared LED light are LED light array.
The automatically controlled sample stage liftable, the elevating control of automatically controlled sample stage plant to be measured is at a distance from camera lens.
The computer, control module and data acquisition module are for realizing automation collection and data processing.Computer
It is issued and is instructed by control module and data acquisition module, it is high to adjust automatically controlled sample stage according to plant size by control module
It spends, select different light source type (measurement light, actinic light, saturated light and far red light) and intensity of illumination and duration, selection filter
Impeller wavelength can also control CCD camera and start to acquire the chlorophyll fluorescence image of plant to be measured；Pass through data acquisition module control
CCD camera processed acquires the chlorophyll fluorescence image information of plant to be measured and is uploaded to computer in real time；Computer passes through analysis leaf
Green element fluoroscopic image data information, is diagnosed by drought stress degree of the plant drought stress decision model to plant.
Compared with prior art, the invention has the benefit that
(1) the present invention is based on the plant drouhgt stress diagnostic methods of imaging-PAM technology can not only provide chlorophyll
Fluorescence parameter, moreover it is possible to which the visualization for being distributed fluorescence parameter in blade space shows photosynthetic work between blade surface and blade
Heterogeneity can at a high speed, in high precision, in real time diagnose drought stress, while testing cost is lower；
(2) the present invention is based on the plant drouhgt stress diagnostic device of imaging-PAM technology be suitable for different type,
The detection of different plants, it is only necessary to adjust lifting platform height and change camera lens.
Detailed description of the invention
Fig. 1 is the structural schematic diagram of plant drouhgt stress diagnostic device of the invention；
Fig. 2 is the structural schematic diagram of light source board.
Wherein, 1, lighting box；2, imaging-PAM module；3, ring support；4, light source board；5, plant to be measured；6,
Automatically controlled sample stage；7, partition；8, control module；9, data acquisition module；10, universal wheel；11, pumping board；12, computer.
Specific embodiment
Present invention is further described in detail with reference to the accompanying drawings and examples.
As shown in Figure 1, plant drouhgt stress diagnostic device of the invention includes: lighting box 1, imaging-PAM module
2, ring support 3, light source board 4, plant to be measured 5, automatically controlled sample stage 6, partition 7, control module 8, data acquisition module 9, universal
Take turns 10, pumping board 11, computer 12.
The inner wall of lighting box 1 sprays pitch-dark and frosted.
Lighting box 1 is divided into upper layer and lower layer by partition 7, and upper layer is the region of Image Acquisition, and lower layer is control system sum number
According to acquisition system region.
As shown in Fig. 2, light source board 4 is " returning " word shape, geometric center hollow out, hollowed out area is greater than the maximum transversal of camera
Face size.Light source board 4 is mounted on the roof of lighting box 1 by ring support 3.On light source board 4 in the geometry of light source board 4
The heart is separately installed with:
Blood orange light LED light, generation wavelength are the measurement light of 620nm；
White LED lamp, generation wavelength are the actinic light and saturated light of 450~465nm；
Far infrared LED light, generation wavelength are the far red light of 740nm.
Blood orange light LED light, white LED lamp and far infrared LED light are LED light array.
The illumination plane of light source board 4 is parallel with automatically controlled sample stage.
The geometric center of light source board 4 is equipped with imaging-PAM module 2, and the module is by CCD camera, camera lens and filtering
Wheel composition, CCD camera resolution ratio is that 1392 × 1040, valid pixel size is 6.45 μm, and lens aperture 1.4, focal length are
6mm is equipped with filter wheel on camera lens.
Imaging-PAM module 2, ring support 3, light source board 4 are located in the image acquisition region on upper layer.
Automatically controlled sample stage 6 is mounted on partition 7, is used for support plant to be measured, and automatically controlled 6 liftable of sample stage is to be measured to adjust
The distance between plant and camera lens.
The output of control module 8 passes through USB data line and automatically controlled sample stage 6, light source board 4, imaging-PAM module 2
Connection, can control the lifting of automatically controlled sample stage 6, also can control the light that light source board 11 selects different light source types, each light source
According to intensity and light irradiation time, filter wheel can also be controlled and select different wavelength, furthermore imaging-PAM module 2 acquires
5 chlorophyll fluorescence image of plant to be measured and its collection period also available control.
The tank wall of lighting box 1 be equipped with can outward pull pumping board 11, pumping board 11 pull out after can place computer 12.Illumination
1 bottom of case is equipped with universal wheel 10, and the movement of whole device may be implemented.
The present embodiment is wild based on the plant that the plant drouhgt stress diagnostic method of imaging-PAM technology is selected
Type AQ arabidopsis, comprising the following steps:
(1) plant height is adjusted, keeping object lens distance is 30cm, by plant dark adaptation 20min to be measured；
(2) 4 week old arabidopsis of at least 500 plants known diagnosis results, including health and the quasi- south of drought stress degree are chosen
Mustard, quantity respectively account for 50%, acquire its chlorophyll fluorescence image information, specifically include: adjustment light source parameters, actinic light are set as
100μmolm-2s-1, saturated light is set as 1100 μm of olm-2s-1, open measurement light pulse and acquire the minimum fluorescence Fo after dark adaptation
Image, then open the maximum fluorescence Fm image after saturated light pulse collection dark adaptation；Actinic light is then turned on, saturation is then opened
Light pulse acquires the maximum fluorescence Fm ' image after light adaptation, then opens the minimum fluorescence after far red light pulse collection light adaptation
Fo ' image finally acquires steady-state fluorescence Fs ' image when light adaptation；Fv/Fm, Φ PS II, NPQ figure are obtained by above-mentioned image
Picture, wherein
Fv/Fm=(Fm-Fo)/Fm；
Φ PS II=(Fm '-Fs ')/Fm '；
NPQ=(Fm-Fm ')/Fm '.
(3) characteristic parameter, including Fv/Fm, Φ PS II, NPQ image are extracted from above-mentioned image information, canopy region
Area, mean value, variance, textural characteristics, bimodal separation value S, space efficiency value Wmax；
Bimodal separation value (S) and space efficiency value (Wmax) obtain in the following manner:
S=(μmax-μmin)/2(σmax+σmin)
Wherein, S is bimodal separation value, μmax、σmaxThe respectively average and standard deviation value in peak value region, μmin、σmin
The respectively average and standard deviation value in low peak region；
Smax=(0.87- μmax)/2σmaxFor the bimodal separation value of efficiency highest part in chlorophyll fluorescence image, Smin=
(0.87-μmin)/2σminFor the bimodal separation value of efficiency lowermost portion in chlorophyll fluorescence image, ρmax、ρminRespectively peak value
The specific gravity for accounting for overall area in region and low peak region, then space efficiency value WmaxValue are as follows:
Wmax=(Smax×ρmax-Smin×ρmin)/Smax×ρmax
(4) residual error method rejecting abnormalities sample is used, by the characteristic parameter of sample according to modeling collection sample and forecast set sample number
The 2: 1 of amount are grouped, and are 3 bases point with vector machine classifier, Naive Bayes Classifier, radial base neural net classifier
Modeling collection is inputted base classifier respectively, adaptively obtains each classification using the thought and K neighbor method of clustering by class device
The Dynamic Weights of device merge to obtain fusion discrimination model finally by linear weighted function, with forecast set sample to the above-mentioned model
It tests, finally establishes plant drought stress decision model；
(5) arabidopsis image to be measured is acquired by step (1)~(3), the characteristic parameter input step (4) of the image is built
It is diagnosed in vertical plant drought stress decision model, 0 indicates that health, 1 indicate drought stress, finally obtains diagnostic result.
Claims (3)
1. a kind of plant drouhgt stress diagnostic method based on imaging-PAM technology, which is characterized in that including following step
It is rapid:
(1) the sample set plant of known drought stress diagnostic result is subjected to dark adaptation, acquires its canopy chlorophyll fluorescence image
Data and the characteristic parameter for extracting chlorophyll fluorescence image；
The chlorophyll fluorescence image data includes:
The sub- efficiency of maximum amount absorbs the maximal efficiency that luminous energy is used to restore plastoquinone QA for characterizing lightsystemⅡ；
Photo-quantum efficiency absorbs the efficiency that luminous energy is used to restore plastoquinone QA for characterizing lightsystemⅡ；
Non- photochemical fluorescent quenching coefficient, for characterizing lightsystemⅡ heat leakage situation；
The characteristic parameter of the chlorophyll fluorescence image includes: the area in canopy region, mean value, variance, textural characteristics, bimodal
Separation value and space efficiency value；
(2) according to the chlorophyll fluorescence image data and characteristic parameter of acquisition, it is based on MATLAB software, characteristic parameter is distinguished defeated
Incoming vector machine classifier, Naive Bayes Classifier and radial base neural net classifier establish base classifier, adaptively obtain
The Dynamic Weights for taking each classifier merge to obtain plant drought stress decision model by linear weighted function；
(3) the chlorophyll fluorescence image data and characteristic parameter of plant to be measured are acquired according to the method for step (1), and it is dry to substitute into plant
Drought stress decision model carries out drought stress diagnosis.
2. plant drouhgt stress diagnostic method according to claim 1, which is characterized in that in step (1), acquire Plant Leaf
Green element fluoroscopic image data, comprising the following steps:
(1-1) opens the minimum fluorescence Fo after measurement light pulse acquisition dark adaptation；
(1-2) opens the maximum fluorescence Fm after the dark adaptation of saturated light pulse collection；
(1-3) opens actinic light, then opens the maximum fluorescence Fm' after saturated light pulse collection light adaptation；
(1-4) opens the minimum fluorescence Fo' after far red light pulse collection light adaptation；
(1-5) finally acquires steady-state fluorescence Fs' when light adaptation.
3. according to plant drouhgt stress diagnostic method according to claim 1, which is characterized in that sample set plant includes strong
The quantity ratio of health plant and drought stressed plants, healthy plant and drought stressed plants is 1:1.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6567537B1 (en) * | 2000-01-13 | 2003-05-20 | Virginia Commonwealth University | Method to assess plant stress using two narrow red spectral bands |
| CN101718683A (en) * | 2009-11-10 | 2010-06-02 | 中国农业大学 | Device for fast detection of chlorophyll content in leaf blades, modeling method and detection method |
| CN102495005A (en) * | 2011-11-17 | 2012-06-13 | 江苏大学 | Method for diagnosing crop water deficit through hyperspectral image technology |
| CN102598986A (en) * | 2012-03-26 | 2012-07-25 | 天津师范大学 | Method for improving chlorophyll fluorescence power of festuca arundinacea under dry condition by cerium |
| CN103278503A (en) * | 2013-04-25 | 2013-09-04 | 浙江大学 | Multi-sensor technology-based grape water stress diagnosis method and system therefor |
| CN104034710A (en) * | 2014-06-25 | 2014-09-10 | 浙江大学 | Chlorophyll fluorescence and imaging technology based plant disease detection method and detection device |
| CN104568887A (en) * | 2015-01-16 | 2015-04-29 | 山东师范大学 | Method or measuring stress of heavy metals on plants by using plant micro-domain chlorophyll fluorescence method |
| CN105548113A (en) * | 2015-12-31 | 2016-05-04 | 浙江大学 | Plant physiology monitoring method based on chlorophyll fluorescence and multispectral image |
| CN105717115A (en) * | 2016-01-31 | 2016-06-29 | 浙江大学 | High-throughput plant phenotype analysis device and method based on optical imaging technique |
-
2016
- 2016-10-31 CN CN201610930119.8A patent/CN106546567B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6567537B1 (en) * | 2000-01-13 | 2003-05-20 | Virginia Commonwealth University | Method to assess plant stress using two narrow red spectral bands |
| CN101718683A (en) * | 2009-11-10 | 2010-06-02 | 中国农业大学 | Device for fast detection of chlorophyll content in leaf blades, modeling method and detection method |
| CN102495005A (en) * | 2011-11-17 | 2012-06-13 | 江苏大学 | Method for diagnosing crop water deficit through hyperspectral image technology |
| CN102598986A (en) * | 2012-03-26 | 2012-07-25 | 天津师范大学 | Method for improving chlorophyll fluorescence power of festuca arundinacea under dry condition by cerium |
| CN103278503A (en) * | 2013-04-25 | 2013-09-04 | 浙江大学 | Multi-sensor technology-based grape water stress diagnosis method and system therefor |
| CN104034710A (en) * | 2014-06-25 | 2014-09-10 | 浙江大学 | Chlorophyll fluorescence and imaging technology based plant disease detection method and detection device |
| CN104568887A (en) * | 2015-01-16 | 2015-04-29 | 山东师范大学 | Method or measuring stress of heavy metals on plants by using plant micro-domain chlorophyll fluorescence method |
| CN105548113A (en) * | 2015-12-31 | 2016-05-04 | 浙江大学 | Plant physiology monitoring method based on chlorophyll fluorescence and multispectral image |
| CN105717115A (en) * | 2016-01-31 | 2016-06-29 | 浙江大学 | High-throughput plant phenotype analysis device and method based on optical imaging technique |
Non-Patent Citations (6)
| Title |
|---|
| Evaluation of Chlorophyll Content and Fluorescence Parameters as indicators of Drought Tolerance in Barley;Li Rong-hua et al.;《Agricultural Sciences in China》;20061031;第5卷(第10期);第751-757页 * |
| 基于自适应权值的多分类器融合方法;刘汝杰 等;《北方交通大学学报》;20010430;第25卷(第2期);第14-17页 * |
| 多种分类器融合的遥感影像分类;张丹 等;《遥感应用》;20090531;第39-42页 * |
| 干旱胁迫对冬小麦叶绿素荧光的影响;张永强 等;《中国生态农业学报》;20021231;第10卷(第4期);第13-15页 * |
| 水分胁迫对不同抗旱类型冬小麦幼苗叶绿素荧光参数的影响;杨晓青 等;《西北植物学报》;20041231;第24卷(第5期);第812-816页 * |
| 水分胁迫对澳洲坚果叶绿素a荧光参数的影响;刘建福 等;《华侨大学学报（自然科学版）》;20030731;第24卷(第3期);第305-309页 * |
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